Inter-trial effects in visual pop-out search: Factorial comparison of Bayesian updating models
Fig 7
Schematic illustration of prior updating and the resulting changes of the starting point.
The top panels show the hyperprior, i.e., the probability distribution on the frequency of target present trials (p), and how it changes over three subsequent trials. The middle panels show the current best estimate of the frequency distribution over target-present and -absent trials (i.e., p and 1 − p). The best estimate of p is defined as the expected value of the hyperprior. The bottom panels show a sketch of the evidence accumulation process where the starting point is set as the log prior odds for the two response options (target- present vs. -absent), computed based on the current best estimate of p. Tp and Ta are the decision thresholds for target-present and -absent responses, respectively, and μp and μa are the respective drift rates. The sketch of the evidence accumulation process is based on the LATER model (rather than the DDM) and therefore shown with a single boundary (that associated with the correct response). Note that the boundary depicted for trial 2 (target absent) is not the same as those for (target-present trials) trials 1 and 3. In the equivalent figure based on the DDM, there would have been two boundaries, and on trial 2, the drift rate would have been negative and the starting point would have been closer to the upper boundary than on the first trial. Note also that this figure illustrates updating with some memory decay (see level 3). Without memory decay, the distribution on trial 3 would be exactly the same as on trial 1.